We present an in silico workflow to streamline the identification of promising N-heterocyclic carbenes (NHCs) as ligands in metal catalysis or as catalysts in organocatalysis. Central to this workflow is the NHC-cracker database, which contains over 200 descriptors for 1781 nonredundant NHCs, each documented as an NHC-metal complex in the Cambridge Structural Database. To demonstrate its utility, we applied it to two catalytic problems using literature data. First, we analyzed 21 Ru–NHC complexes active in the ethenolysis of cyclic olefins. An MLR (multivariate linear regression) model trained on 11 Ru complexes based on NHCs in NHC-cracker successfully rationalized the behavior of the remaining 10 complexes. Second, we examined an Ir–Ni dual-catalyzed Csp2–Csp3 cross-coupling reaction involving five experimentally tested NHC skeletons. Using a multiscale workflow, we created DFT-based data sets to train two MLR models: one for productive substrate activation and another for detrimental NHC dimerization. Consistent with experiments, the models identified oxazoles as reactive, while benzimidazoles, triazoles, thiazoles, and untested cyclic (alkyl)(amino)carbenes were predicted as nonreactive. Experimental validation confirmed the latter’s lack of productive substrate activation, supporting the proposed mechanistic scenario.