Multimodality plays a key role as one of the most challenging problem characteristics in the common understanding of solving optimization tasks. Based on insights from the single-objective optimization domain, local optima are considered to be (deceptive) traps for optimization approaches such as gradient descent or different kinds of neighborhood search. Consequently, as continuous multi-objective (MO) problems usually combine multiple (often multimodal) single-objective problems, multimodality is considered an important challenge for MO problems as well. In fact, even very simple MO problems possess a multimodal landscape due to the interactions among its objectives. Thus, modern benchmarks name multimodality an important problem characteristic, while at the same time, heuristic algorithms (like evolutionary algorithms) are expected to be almost mandatory for handling multimodality in an effective way. Here, we continue our previous work by (1) formally defining multimodality in the continuous MO setting, (2) provide techniques for visualizing landscapes of continuous MO problems—not only in the objective but also in the decision space—to improve the intuition regarding continuous MO multimodality, and (3) analyze MO problems based on examples from an extensive test-bed. Thereby, we provide the tools for displaying and detecting basins of attraction, as well as superpositions of local optima, in the decision space of the landscapes. Most important, and maybe unexpected, we are able to show that multimodality in continuous MO optimization differs largely from our understanding of multimodality in the single-objective domain: for simple MO optimization approaches, local efficient sets are often no traps. Instead, locality can even be exploited to slide toward the global efficient set.