Automated benchmarking environments aim to support researchers in understanding how different algorithms perform on different types of optimization problems. Such comparisons carry the potential to provide insights into the strengths and weaknesses of different approaches, which can be leveraged into designing new algorithms. Carefully selected benchmark problems are also needed as training sets in the context of algorithm selection and configuration. With the ultimate goal to create a meaningful benchmark set for iterative optimization heuristics, we compile and assess in this work a selection of discrete optimization problems that subscribe to different types of fitness landscapes. All problems have been implemented and tested within IOHprofiler, our recently released software built to assess iterative heuristics solving combinatorial optimization problems. For each selected problem we compare performances of eleven different heuristics. Apart from fixed-target and fixed-budget results for the individual problems, we also derive ECDF results for groups of problems. To obtain these, we have implemented an add-on for IOHprofiler which allows aggregation of performance data across different benchmark functions.