Prior studies from our group have addressed the problem of predicting SCO behavior from different angles: using a genetic algorithm (GA) and artificial neural networks (ANNs) trained on different amounts of HF exchange 120 , assessing consensus between a set of 23 density functional approximations (DFAs) , and mining the literature 121 to predict SCO behavior in complexes. We also demonstrated 122 the utility of ANNs trained on hybrid DFT bond lengths, as these ANNs correctly assigned the majority of spin states in a set of 46 Fe(II) SCO complexes data-mined from literature. Following the text-mining protocol from the prior study, 122 we now curate an even larger data set of 95 Fe(II) SCO complexes from the Cambridge Structural Database 123 (CSD) with a diverse set of ligands and confirm the experimental observation of SCO behavior with a combination of literature and structural analysis that we refer to as SCO-95. We evaluate spin splitting energies between HS and LS states and transition temperatures using 23 DFAs across various rungs of "Jacob's ladder" 124 from semi-local generalized-gradient approximations to double hybrids along with seven variants of the B3LYP functional. We evaluate the role of thermodynamic corrections in selecting the best DFA based on spin splitting free energies. Ultimately, T1/2 is the most essential quantity to predict for SCO behavior, and we compare predictions of DFAs to T1/2 values curated from the literature for all our complexes, highlighting the challenges that remain for accurate prediction of SCO behavior by DFT.