I can think of two things right now:
1. Go back to field and collect some more conjoint data with brand included. You can combine both your first wave (with conjoint) of conjoint data with the second wave (without brand) using HB. You would need to do some data processing of the .CSV files to make the first wave of data have a brand column in the data file, but the brands would be missing each time. Then, use CBC/HB analysis to analyze the combined .CSV file (I'm assuming you used CBC for your conjoint analysis) to estimate all the parameters (including brand) for all the respondents. HB would perform what essentially is missing variable imputation for the respondents who didn't see brand, based on the means and covariances of the respondents who did see the brands with the other attributes.
2. If you strongly believe that the only thing you are missing is brand, and all other aspects of the conjoint predictions really should be predictive of market share (you have sampled respondents well to represent the market, equal time on the market, equal information, equal effectiveness of the sales force, etc.), then you could ask our Market Simulator software to come up with the External Effect adjustments (the additive part-worth constants) for each product in the simulator such that the simulated shares of preference perfectly match market shares. This is a major leap of faith and probably fairly dangerous to do.