Compared to existing technologies, thermodynamic cycles based on supercritical carbon dioxide (sCO₂) are leading to higher efficiencies and reduced component sizes. However, it is possible to further improve the performance of sCO₂ power cycles by using mixtures of CO₂ with suitable additives, as also discussed in the literature for different applications. This work investigates the potential to optimize the characteristics of sCO₂ power cycles by selectively adding different substances in varying amounts to CO₂. A new methodology is proposed: By using the reference equation of state for CO₂ in combination with a multi-fluid mixture model, a theoretical screening of suitable additives was done. In the literature, studies were mainly limited to mixtures for which adjusted mixture models are available. By contrast, in this work the use of a predictive mixture model, which was recently developed at our institute, also allows to include fluids, for which no adjusted models are available. Applied to two thermodynamic cycles, changes in efficiency compared to the use of pure CO₂ have been evaluated. Several promising mixture candidates have been identified. Additionally, individual effects on the cycle characteristics as well as shifts of the critical points have been investigated and are discussed.