We present a customized analysis of landscape features for predicting the performance of multi-objective combinatorial optimization algorithms. We consider features from the recently proposed compressed Pareto local optimal solutions networks (\cnet) model of combinatorial landscapes. The benchmark instances are a set of rhomnk-landscapes with 2 and 3 objectives and various levels of ruggedness and objective correlation. We evaluate the predictive power of these features for three algorithms -- Pareto Local Search (PLS), Global Simple EMO Optimizer (GSEMO), and Non-dominated Sorting Genetic Algorithm (NSGA-II) -- using resolution and hypervolume as performance metrics. Our tailored analysis reveals feature combinations that influence algorithm performance and highlights challenges specific to certain landscapes. This study provides deeper insights into feature importance, tailored to specific rhomnk-landscape and algorithms.