In this paper, we propose the Fault Impact Map (FIM) as a neural network post-training method measuring the accuracy impact of faults or stuck devices in memristive crossbar computing. Memristive crossbars enable highly energy-efficient neural network computation, but stuck-at-faults (SAF) can affect the weight representation and significantly reduce network performance. Recent studies consider sensitivity-based or randomly injected SAF and propose various mapping or training methods to improve the network performance. However, the not unlikely worst case of SAF is not taken into account, which leads to an overestimation of the robustness of the network. We introduce a novel brute-force FIM algorithm to detect the SAF worst-case scenario of a trained memristive crossbar neural network and determine the weights that are most important for maintaining its test accuracy. The FIM algorithm measures the accuracy drop caused by successively faulty weights or biases leading to a drastic accuracy drop. For an MNIST classification performed on a quantized 2-layer memristive crossbar neural network, we compare the FIM with state-of-the-art methods like gradient-based sensitivity analysis and random fault injection and discuss the prediction of the network vulnerability to SAF. Our test results show, that SAF of less than 1% of the neural network parameters are sufficient to shrink the test accuracy down to the guess line. Finally, we discuss the computationally effort of the FIM as well as its potential application to increase the network robustness.