We introduced a novel methodology for constructing and disentangling catalytic reaction networks and applied it to the analysis of CO2 conversion into value-added chemicals. Our methodology merges rapid semi-empirical explorations with accurate DFT modeling to understand diverse reaction pathways. It speeds up the identification of key intermediates and clarifies catalytic mechanisms, providing deep insights into catalytic behavior. Moreover, our study provides a more realistic representation of nanostructured catalysts, including facet and edge sites as well as facile diffusion of adsorbates between them, which is shown to be crucial for the understanding of CO2 hydrogenation kinetics and rational design of catalysts for this reaction. The importance of considering both facets and edges in nanoparticle models cannot be overstated, as they account for the diversity of active sites in real-world catalysts, thereby enhancing our understanding of catalytic behavior and aiding in the rational design of materials with optimized surface features.