Recent papers have reported on successful application of constraint solving techniques to off-line real-time scheduling problems, with realistic size and complexity. Success allegedly came for two reasons: major recent advances in solvers efficiency and use of optimized, problem-specific constraint representations. Our current objective is to assess further the range of applicability and the scalability of such constraint solving techniques based on a more general and agnostic evaluation campaign. For this, we have considered a large number of synthetic scheduling problems and a few real-life ones, and attempted to solve them using 3 state-of-the-art solvers, namely CPLEX, Yices2, and MiniZinc/G12. Our findings were that, for all problems considered, constraint solving does scale to a certain limit, then diverges rapidly. This limit greatly depends on the specificity of the scheduling problem type. All experimental data (synthetic task systems, SMT/ILP models) are provided so as to allow experimental reproducibility.